Survey on deep learning in multimodal medical imaging for cancer detection

TL;DR

This survey reviews over 150 recent deep learning approaches for multimodal cancer detection, highlighting challenges like lesion variability and data annotation, and discusses future research directions in the field.

Contribution

It provides a comprehensive overview of deep learning methods for multimodal cancer detection, analyzing datasets, challenges, and solutions, and suggesting future research pathways.

Findings

Deep learning has advanced multimodal cancer detection significantly.

Challenges include lesion variability, data annotation, and imaging artifacts.

The survey identifies promising directions for future research.

Abstract

The task of multimodal cancer detection is to determine the locations and categories of lesions by using different imaging techniques, which is one of the key research methods for cancer diagnosis. Recently, deep learning-based object detection has made significant developments due to its strength in semantic feature extraction and nonlinear function fitting. However, multimodal cancer detection remains challenging due to morphological differences in lesions, interpatient variability, difficulty in annotation, and imaging artifacts. In this survey, we mainly investigate over 150 papers in recent years with respect to multimodal cancer detection using deep learning, with a focus on datasets and solutions to various challenges such as data annotation, variance between classes, small-scale lesions, and occlusion. We also provide an overview of the advantages and drawbacks of each approach. Finally, we discuss the current scope of work and provide directions for the future development of multimodal cancer detection.